Nonprofits in a Time of Turbulence: Challenges and Opportunities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract We have entered a period of turbulent economic and political change. Internationally, slower growth coupled with youth unemployment and rising inequality have driven a renewed interest in social policy. In the US, the preferred policy approach since the 1990s has been to move away from cash assistance to direct service provision spurring demand for nonprofit services at the local level (Smith 2015, “Managing Human Service Organizations in the 21 st Century.” Human Service Organizations: Management, Leadership, & Governance 39 (5):407–411). Recently, however, we have observed a power backlash against trade, immigration and economic insecurity that is reshaping politics and bringing about significant cuts in social service programs and health care at a time when the need is high. Fiscal scarcity will no doubt create an additional burden for nonprofits working with communities in need. In Canada, the federal government is moving in the opposite direction with greater investment in the social policy fields, including healthcare, childcare, housing and poverty reduction initiative. These investments will mean a greater flow of resources to the nonprofit sector, but the government has been clear that in exchange they want to tie funding to results and performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it